Lung cancer diagnosis increasingly relies on advanced medical imaging systems and expert interpretation across heterogeneous clinical data sources. However, accurate clinical decision-making remains challenging due to variations in expert judgment and the complexity of extracting discriminative patterns from electronic health records and radiological datasets. To address these challenges, this paper proposes an enhanced lung cancer classification framework that leverages intensity-driven region-of-interest (RoI) selection from publicly available benchmark datasets, including the Lung Image Database Consortium Image Collection (LIDC-IDRI) and The Cancer Imaging Archive (TCIA). The proposed methodology incorporates customized label refinement and precise annotation of vulnerable RoI regions to capture clinically relevant features associated with malignant nodules. A high-dimensional RoI mapping strategy is employed to improve feature representation and discrimination. Furthermore, a feedback-driven optimization mechanism is integrated within a transfer learning framework to iteratively refine model parameters and enhance learning stability. The optimized RoI representations are transferred to customized deep learning models, enabling efficient knowledge reuse and robust decision-making. The proposed approach is implemented using the CoVNet architecture and evaluated under a 60:40 training-testing split. Experimental results demonstrate a classification accuracy of 97.84%, validating the effectiveness of the proposed framework in improving predictive performance for lung cancer classification.
Syed Thouheed Ahmed, T. Y. Satheesha, L. H. Nagaraja et al.· BMC Medical Informatics and...· 0 citations
The Intrusion Detection Systems (IDSs) are very important tools for defending a network against emerging cyber threats. This study proposes the hybrid intrusion detection system model of extreme gradient boosting (XGBoost) and KMeans++ clustering algorithm to balance the trade-off between accuracy, efficiency, and robustness in detecting malicious traffic. XGBoost algorithms are good for structured problems where classification problems occur, whereas KMeans++ helps you to get more clustering accuracy by helping centroid initialization. For enhancing the performance of the model some feature extraction steps and data preprocessing steps like normalization, encoding, Synthetic Minority Over-sampling Technique (SMOTE) based imbalance data balancing were considered. The system was trained and validated on Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS) 2017 and put to test in various metrics: accuracy, precision, recall, F1-score, ROC-AUC and false positive rate. Comparative analysis was performed using traditional machine learning models such as SVM, Decision Tree, Random Forest, Naive Bayes and deep learning architectures which include CNN, LSTM and Auto-Encoder. It was found to be high detection accuracy of 99.87% with very low FPR by far i.e. 0.1%. The model provided high recall and precision in different types of attack and successful overfitting resistance could be confirmed using 10-fold cross-validation, XGBoost regularization and structure clustering. This work shall play an important role in improving hybrid models to minimize alert fatigue with trustworthy threat classification in the real operational traffic.
Premananda Sahu, V. Himthani, Ashwani Kumar· International Research Journ...· 0 citations